Back in 2014, fivethiryeight.com published an article on alchohol consumption in different countries. The data drinks is available as part of the fivethirtyeight package. Make sure you have installed the fivethirtyeight package before proceeding.
library(fivethirtyeight)
data(drinks)
# or download directly
# alcohol_direct <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/alcohol-consumption/drinks.csv")What are the variable types? Any missing values we should worry about?
No missing values.
| Column name | country | beer_servings | spirit_servings | wine_servings | total_litres_of_pure_alcohol |
|---|---|---|---|---|---|
| Variable type | character | integer | integer | integer | double |
glimpse(drinks)## Rows: 193
## Columns: 5
## $ country <chr> "Afghanistan", "Albania", "Algeria", "And…
## $ beer_servings <int> 0, 89, 25, 245, 217, 102, 193, 21, 261, 2…
## $ spirit_servings <int> 0, 132, 0, 138, 57, 128, 25, 179, 72, 75,…
## $ wine_servings <int> 0, 54, 14, 312, 45, 45, 221, 11, 212, 191…
## $ total_litres_of_pure_alcohol <dbl> 0.0, 4.9, 0.7, 12.4, 5.9, 4.9, 8.3, 3.8, …
skim(drinks)| Name | drinks |
| Number of rows | 193 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 3 | 28 | 0 | 193 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| beer_servings | 0 | 1 | 106.16 | 101.14 | 0 | 20.0 | 76.0 | 188.0 | 376.0 | ▇▃▂▂▁ |
| spirit_servings | 0 | 1 | 80.99 | 88.28 | 0 | 4.0 | 56.0 | 128.0 | 438.0 | ▇▃▂▁▁ |
| wine_servings | 0 | 1 | 49.45 | 79.70 | 0 | 1.0 | 8.0 | 59.0 | 370.0 | ▇▁▁▁▁ |
| total_litres_of_pure_alcohol | 0 | 1 | 4.72 | 3.77 | 0 | 1.3 | 4.2 | 7.2 | 14.4 | ▇▃▅▃▁ |
Make a plot that shows the top 25 beer consuming countries
drinks %>%
slice_max ( order_by = beer_servings, n=25 ) %>% # taking top 25 countries by servings
ggplot(aes(x = beer_servings, y = fct_reorder(country, beer_servings))) +
geom_col(fill="orange") +
labs(
title = "Top 25 Beer Serving Countries in 2010",
subtitle = "Standard Servings Per Person",
x = "Beer Servings (in cans)",
y = "Country"
)Make a plot that shows the top 25 wine consuming countries
drinks %>%
slice_max ( order_by = wine_servings, n=25 ) %>% # taking top 25 countries by servings
ggplot(aes(x = wine_servings, y = fct_reorder(country, wine_servings))) +
geom_col(fill="dark red") +
labs(
title = "Top 25 Wine Serving Countries in 2010",
subtitle = "Standard Servings Per Person",
x = "Wine Servings (in glasses)",
y = "Country"
)Finally, make a plot that shows the top 25 spirit consuming countries
drinks %>%
slice_max ( order_by = spirit_servings, n=25 ) %>% # taking top 25 countries by servings
ggplot(aes(x = spirit_servings, y = fct_reorder(country, spirit_servings))) +
geom_col(fill="grey") +
labs(
title = "Top 25 Spirit Serving Countries in 2010",
subtitle = "Servings (in shots) Per Person",
x = "Spirit Servings",
y = "Country"
)What can you infer from these plots? Don’t just explain what’s in the graph, but speculate or tell a short story (1-2 paragraphs max).
TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.
European countries are high consumers of wine.
Beer is more evenly distributed around the world in the top 25 countries, as compared to wine and spirit.
European countries are higher ranked for overall consumption of beer, wine and spirit.
We will look at a subset sample of movies, taken from the Kaggle IMDB 5000 movie dataset
Besides the obvious variables of title, genre, director, year, and duration, the rest of the variables are as follows:
gross : The gross earnings in the US box office, not adjusted for inflationbudget: The movie’s budgetcast_facebook_likes: the number of facebook likes cast members receivedvotes: the number of people who voted for (or rated) the movie in IMDBreviews: the number of reviews for that movierating: IMDB average ratingmovies <- read_csv(here::here("data", "movies.csv"))
glimpse(movies)## Rows: 2,961
## Columns: 11
## $ title <chr> "Avatar", "Titanic", "Jurassic World", "The Avenge…
## $ genre <chr> "Action", "Drama", "Action", "Action", "Action", "…
## $ director <chr> "James Cameron", "James Cameron", "Colin Trevorrow…
## $ year <dbl> 2009, 1997, 2015, 2012, 2008, 1999, 1977, 2015, 20…
## $ duration <dbl> 178, 194, 124, 173, 152, 136, 125, 141, 164, 93, 1…
## $ gross <dbl> 7.61e+08, 6.59e+08, 6.52e+08, 6.23e+08, 5.33e+08, …
## $ budget <dbl> 2.37e+08, 2.00e+08, 1.50e+08, 2.20e+08, 1.85e+08, …
## $ cast_facebook_likes <dbl> 4834, 45223, 8458, 87697, 57802, 37723, 13485, 920…
## $ votes <dbl> 886204, 793059, 418214, 995415, 1676169, 534658, 9…
## $ reviews <dbl> 3777, 2843, 1934, 2425, 5312, 3917, 1752, 1752, 35…
## $ rating <dbl> 7.9, 7.7, 7.0, 8.1, 9.0, 6.5, 8.7, 7.5, 8.5, 7.2, …
# no missing values. There are Duplicates (2907 distinct titles in 2961 rows).
skim(movies)| Name | movies |
| Number of rows | 2961 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| title | 0 | 1 | 1 | 83 | 0 | 2907 | 0 |
| genre | 0 | 1 | 5 | 11 | 0 | 17 | 0 |
| director | 0 | 1 | 3 | 32 | 0 | 1366 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1 | 2.00e+03 | 9.95e+00 | 1920.0 | 2.00e+03 | 2.00e+03 | 2.01e+03 | 2.02e+03 | ▁▁▁▂▇ |
| duration | 0 | 1 | 1.10e+02 | 2.22e+01 | 37.0 | 9.50e+01 | 1.06e+02 | 1.19e+02 | 3.30e+02 | ▃▇▁▁▁ |
| gross | 0 | 1 | 5.81e+07 | 7.25e+07 | 703.0 | 1.23e+07 | 3.47e+07 | 7.56e+07 | 7.61e+08 | ▇▁▁▁▁ |
| budget | 0 | 1 | 4.06e+07 | 4.37e+07 | 218.0 | 1.10e+07 | 2.60e+07 | 5.50e+07 | 3.00e+08 | ▇▂▁▁▁ |
| cast_facebook_likes | 0 | 1 | 1.24e+04 | 2.05e+04 | 0.0 | 2.24e+03 | 4.60e+03 | 1.69e+04 | 6.57e+05 | ▇▁▁▁▁ |
| votes | 0 | 1 | 1.09e+05 | 1.58e+05 | 5.0 | 1.99e+04 | 5.57e+04 | 1.33e+05 | 1.69e+06 | ▇▁▁▁▁ |
| reviews | 0 | 1 | 5.03e+02 | 4.94e+02 | 2.0 | 1.99e+02 | 3.64e+02 | 6.31e+02 | 5.31e+03 | ▇▁▁▁▁ |
| rating | 0 | 1 | 6.39e+00 | 1.05e+00 | 1.6 | 5.80e+00 | 6.50e+00 | 7.10e+00 | 9.30e+00 | ▁▁▆▇▁ |
# show the duplicate movies
movies %>% count(title, sort=T)## # A tibble: 2,907 × 2
## title n
## <chr> <int>
## 1 Home 3
## 2 A Nightmare on Elm Street 2
## 3 Across the Universe 2
## 4 Alice in Wonderland 2
## 5 Aloha 2
## 6 Around the World in 80 Days 2
## 7 Brothers 2
## 8 Carrie 2
## 9 Chasing Liberty 2
## 10 Cinderella 2
## # … with 2,897 more rows
# to see what happens with the duplicates
movies %>% filter(title=="Home")## # A tibble: 3 × 11
## title genre director year duration gross budget cast_facebook_l… votes
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Home Adventure Tim John… 2015 94 1.77e8 1.35e8 17883 70121
## 2 Home Adventure Tim John… 2015 94 1.77e8 1.35e8 17883 70133
## 3 Home Adventure Tim John… 2015 94 1.77e8 1.35e8 17883 70136
## # … with 2 more variables: reviews <dbl>, rating <dbl>
# `distinct` function can only keep the first entry but not latest
# movies <- distinct(movies, title, .keep_all=T)
length(unique(movies$title))## [1] 2907
movies <- movies %>%
group_by(title) %>%
filter(votes == max(votes)) %>%
ungroup()
# there are still duplicates
movies %>% count(title, sort=T)## # A tibble: 2,907 × 2
## title n
## <chr> <int>
## 1 Chasing Liberty 2
## 2 10 Cloverfield Lane 1
## 3 10 Days in a Madhouse 1
## 4 10 Things I Hate About You 1
## 5 102 Dalmatians 1
## 6 10th & Wolf 1
## 7 12 Rounds 1
## 8 12 Years a Slave 1
## 9 127 Hours 1
## 10 13 Going on 30 1
## # … with 2,897 more rows
# to see what happens with the duplicates
movies %>% filter(title=="Chasing Liberty")## # A tibble: 2 × 11
## title genre director year duration gross budget cast_facebook_l… votes
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Chasing… Comedy Andy Cad… 2004 101 1.22e7 2.3e7 842 30092
## 2 Chasing… Comedy Andy Cad… 2004 101 1.22e7 2.3e7 829 30092
## # … with 2 more variables: reviews <dbl>, rating <dbl>
# do the filter only for the entries of Chasing Liberty
movies <- movies %>%
group_by(title) %>%
filter(cast_facebook_likes==max(cast_facebook_likes)) %>%
ungroup()
skim(movies)| Name | movies |
| Number of rows | 2907 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 8 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| title | 0 | 1 | 1 | 83 | 0 | 2907 | 0 |
| genre | 0 | 1 | 5 | 11 | 0 | 17 | 0 |
| director | 0 | 1 | 3 | 32 | 0 | 1366 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1 | 2.00e+03 | 9.92e+00 | 1920.0 | 2.00e+03 | 2.00e+03 | 2.01e+03 | 2.02e+03 | ▁▁▁▂▇ |
| duration | 0 | 1 | 1.10e+02 | 2.23e+01 | 37.0 | 9.50e+01 | 1.05e+02 | 1.19e+02 | 3.30e+02 | ▃▇▁▁▁ |
| gross | 0 | 1 | 5.76e+07 | 7.23e+07 | 703.0 | 1.20e+07 | 3.45e+07 | 7.51e+07 | 7.61e+08 | ▇▁▁▁▁ |
| budget | 0 | 1 | 4.02e+07 | 4.32e+07 | 218.0 | 1.10e+07 | 2.50e+07 | 5.50e+07 | 3.00e+08 | ▇▂▁▁▁ |
| cast_facebook_likes | 0 | 1 | 1.23e+04 | 2.05e+04 | 0.0 | 2.22e+03 | 4.54e+03 | 1.68e+04 | 6.57e+05 | ▇▁▁▁▁ |
| votes | 0 | 1 | 1.09e+05 | 1.59e+05 | 5.0 | 1.95e+04 | 5.47e+04 | 1.32e+05 | 1.69e+06 | ▇▁▁▁▁ |
| reviews | 0 | 1 | 4.98e+02 | 4.93e+02 | 2.0 | 1.97e+02 | 3.58e+02 | 6.24e+02 | 5.31e+03 | ▇▁▁▁▁ |
| rating | 0 | 1 | 6.39e+00 | 1.06e+00 | 1.6 | 5.80e+00 | 6.50e+00 | 7.10e+00 | 9.30e+00 | ▁▁▆▇▁ |
movies %>% count(genre, sort = TRUE)## # A tibble: 17 × 2
## genre n
## <chr> <int>
## 1 Comedy 844
## 2 Action 719
## 3 Drama 484
## 4 Adventure 281
## 5 Crime 198
## 6 Biography 135
## 7 Horror 128
## 8 Animation 35
## 9 Fantasy 26
## 10 Documentary 25
## 11 Mystery 15
## 12 Sci-Fi 7
## 13 Family 3
## 14 Musical 2
## 15 Romance 2
## 16 Western 2
## 17 Thriller 1
gross and budget) by genre. Calculate a variable return_on_budget which shows how many $ did a movie make at the box office for each $ of its budget. Ranked genres by this return_on_budget in descending ordermovies %>%
mutate(movies_return = gross/budget ) %>%
group_by(genre) %>%
summarise(avg_gross = mean(gross),
avg_budget = mean(budget),
genre_return_on_budget = sum(gross)/sum(budget),
movie_mean_return_on_budget = mean(movies_return)) %>%
arrange(-movie_mean_return_on_budget)## # A tibble: 17 × 5
## genre avg_gross avg_budget genre_return_on_budget movie_mean_return_o…
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Horror 37782310. 13804379. 2.74 86.1
## 2 Biography 45201805. 28543696. 1.58 22.3
## 3 Musical 92084000 3189500 28.9 18.8
## 4 Family 149160478. 14833333. 10.1 14.1
## 5 Documentary 17353973. 5887852. 2.95 8.70
## 6 Western 20821884 3465000 6.01 7.06
## 7 Fantasy 41902674. 18484615. 2.27 6.10
## 8 Animation 98433792. 61701429. 1.60 5.01
## 9 Comedy 42487808. 24458506. 1.74 3.70
## 10 Romance 31264848. 25107500 1.25 3.17
## 11 Drama 36754959. 25832605. 1.42 2.98
## 12 Mystery 69117136. 41500000 1.67 2.90
## 13 Adventure 94350236. 64692313. 1.46 2.44
## 14 Crime 37601525. 26527405. 1.42 2.19
## 15 Action 86270343. 70774558. 1.22 1.93
## 16 Sci-Fi 29788371. 27607143. 1.08 1.58
## 17 Thriller 2468 300000 0.00823 0.00823
movies %>%
group_by(director) %>%
summarise(total_gross = sum(gross),
mean_gross = mean(gross),
median_gross = median(gross),
standard_dev_gross = sd(gross)) %>%
slice_max ( order_by = total_gross, n = 15)## # A tibble: 15 × 5
## director total_gross mean_gross median_gross standard_dev_gross
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Steven Spielberg 4014061704 174524422. 164435221 101421051.
## 2 Michael Bay 2195443511 182953626. 168468240. 125789167.
## 3 James Cameron 1909725910 318287652. 175562880. 309171337.
## 4 Christopher Nolan 1813227576 226653447 196667606. 187224133.
## 5 George Lucas 1741418480 348283696 380262555 146193880.
## 6 Robert Zemeckis 1619309108 124562239. 100853835 91300279.
## 7 Tim Burton 1557078534 111219895. 69791834 99304293.
## 8 Sam Raimi 1443167519 180395940. 138480208 174705230.
## 9 Clint Eastwood 1378321100 72543216. 46700000 75487408.
## 10 Francis Lawrence 1358501971 271700394. 281666058 135437020.
## 11 Ron Howard 1335988092 111332341 101587923 81933761.
## 12 Gore Verbinski 1329600995 189942999. 123207194 154473822.
## 13 Andrew Adamson 1137446920 284361730 279680930. 120895765.
## 14 Shawn Levy 1129750988 102704635. 85463309 65484773.
## 15 Ridley Scott 1128857598 80632686. 47775715 68812285.
movies_rating <- movies %>%
group_by(genre) %>%
summarise(mean_rating = mean(rating),
min_rating = min(rating),
max_rating = max(rating),
sd_rating = sd(rating))
movies_rating## # A tibble: 17 × 5
## genre mean_rating min_rating max_rating sd_rating
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Action 6.23 2.1 9 1.04
## 2 Adventure 6.51 2.3 8.6 1.11
## 3 Animation 6.65 4.5 8 0.968
## 4 Biography 7.11 4.5 8.9 0.760
## 5 Comedy 6.11 1.9 8.8 1.02
## 6 Crime 6.92 4.8 9.3 0.853
## 7 Documentary 6.66 1.6 8.5 1.77
## 8 Drama 6.74 2.1 8.8 0.915
## 9 Family 6.5 5.7 7.9 1.22
## 10 Fantasy 6.08 4.3 7.9 0.953
## 11 Horror 5.79 3.6 8.5 0.987
## 12 Musical 6.75 6.3 7.2 0.636
## 13 Mystery 6.84 4.6 8.5 0.910
## 14 Romance 6.65 6.2 7.1 0.636
## 15 Sci-Fi 6.66 5 8.2 1.09
## 16 Thriller 4.8 4.8 4.8 NA
## 17 Western 5.7 4.1 7.3 2.26
movies %>%
ggplot(mapping = aes(x = rating)) +
geom_histogram(bins=30) +
facet_wrap(~genre)+
labs(title = "Distribution of ratings in each genre",
x = "Rating (1-10)",
y = "Num of movies")+
NULLggplot to answer the followinggross and cast_facebook_likes. Produce a scatterplot and write one sentence discussing whether the number of facebook likes that the cast has received is likely to be a good predictor of how much money a movie will make at the box office. What variable are you going to map to the Y- and X- axes?ggplot(movies, aes(x = cast_facebook_likes, y = gross)) +
geom_point() +
geom_smooth(method = "lm")+
NULLgross and budget. Produce a scatterplot and write one sentence discussing whether budget is likely to be a good predictor of how much money a movie will make at the box office.ggplot(movies, aes(x = budget , y = gross)) +
geom_point() +
geom_smooth(method = "lm")The budget and gross do seem correlated. The higher the budget, it is more likely that the gross may be higher.
Examine the relationship between gross and rating. Produce a scatterplot, faceted by genre and discuss whether IMDB ratings are likely to be a good predictor of how much money a movie will make at the box office. Is there anything strange in this dataset?
ggplot(movies, aes(x = rating , y = gross)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~genre) +
labs(title = "Gross vs Rating of Movies For Each Genre ",
x = "Rating",
y = "Gross") +
NULLWe can see that:
The higher the rating the more will be the gross for the most genres of movies.
For movies of some genres like ‘Documentary’, ‘Mystery’, ‘Horror’ and ‘Sci-Fi’, the gross has a very less change with respect to rating. Documentaries certainly have a different business model.
Negative correlation even appears.
You may find useful the material on finance data sources.
We will use the tidyquant package to download historical data of stock prices, calculate returns, and examine the distribution of returns.
We must first identify which stocks we want to download data for, and for this we must know their ticker symbol; Apple is known as AAPL, Microsoft as MSFT, McDonald’s as MCD, etc. The file nyse.csv contains 508 stocks listed on the NYSE, their ticker symbol, name, the IPO (Initial Public Offering) year, and the sector and industry the company is in.
nyse <- read_csv(here::here("data","nyse.csv"))
glimpse(nyse)## Rows: 508
## Columns: 6
## $ symbol <chr> "MMM", "ABB", "ABT", "ABBV", "ACN", "AAP", "AFL", "A", "…
## $ name <chr> "3M Company", "ABB Ltd", "Abbott Laboratories", "AbbVie …
## $ ipo_year <chr> "n/a", "n/a", "n/a", "2012", "2001", "n/a", "n/a", "1999…
## $ sector <chr> "Health Care", "Consumer Durables", "Health Care", "Heal…
## $ industry <chr> "Medical/Dental Instruments", "Electrical Products", "Ma…
## $ summary_quote <chr> "https://www.nasdaq.com/symbol/mmm", "https://www.nasdaq…
nyse$sector## [1] "Health Care" "Consumer Durables" "Health Care"
## [4] "Health Care" "Miscellaneous" "Consumer Services"
## [7] "Finance" "Capital Goods" "Basic Industries"
## [10] "Basic Industries" "Health Care" "Consumer Services"
## [13] "Miscellaneous" "Finance" "Health Care"
## [16] "Finance" "Finance" "Consumer Services"
## [19] "Consumer Non-Durables" "Consumer Non-Durables" "Consumer Durables"
## [22] "Public Utilities" "Public Utilities" "Public Utilities"
## [25] "Public Utilities" "Finance" "Finance"
## [28] "Consumer Services" "Public Utilities" "Finance"
## [31] "Health Care" "Capital Goods" "Consumer Durables"
## [34] "Consumer Non-Durables" "Consumer Services" "Health Care"
## [37] "Finance" "Finance" "Capital Goods"
## [40] "Consumer Services" "Basic Industries" "Consumer Non-Durables"
## [43] "Capital Goods" "Technology" "Finance"
## [46] "Health Care" "Public Utilities" "Public Utilities"
## [49] "Technology" "Consumer Services" "Consumer Services"
## [52] "Public Utilities" "Finance" "Energy"
## [55] "Consumer Durables" "Finance" "Finance"
## [58] "Finance" "Finance" "Finance"
## [61] "Finance" "Finance" "Finance"
## [64] "Finance" "Finance" "Finance"
## [67] "Finance" "Finance" "Basic Industries"
## [70] "Health Care" "Finance" "Public Utilities"
## [73] "Health Care" "Consumer Services" "Basic Industries"
## [76] "Energy" "Capital Goods" "Finance"
## [79] "Capital Goods" "Consumer Services" "Consumer Services"
## [82] "Health Care" "Energy" "Health Care"
## [85] "Consumer Non-Durables" "Miscellaneous" "Consumer Services"
## [88] "Consumer Services" "Finance" "Public Utilities"
## [91] "Consumer Services" "Energy" "Consumer Services"
## [94] "Consumer Non-Durables" "Finance" "Transportation"
## [97] "Energy" "Transportation" "Miscellaneous"
## [100] "Finance" "Health Care" "Consumer Durables"
## [103] "Consumer Services" "Consumer Services" "Consumer Durables"
## [106] "Capital Goods" "Finance" "Consumer Services"
## [109] "Basic Industries" "Energy" "Health Care"
## [112] "Public Utilities" "Public Utilities" "Public Utilities"
## [115] "Basic Industries" "Consumer Services" "Energy"
## [118] "Consumer Services" "Finance" "Public Utilities"
## [121] "Energy" "Public Utilities" "Public Utilities"
## [124] "Consumer Services" "Finance" "Public Utilities"
## [127] "Basic Industries" "Health Care" "Finance"
## [130] "Finance" "Consumer Durables" "Public Utilities"
## [133] "Finance" "Capital Goods" "Consumer Non-Durables"
## [136] "Consumer Non-Durables" "Consumer Non-Durables" "Consumer Non-Durables"
## [139] "Consumer Non-Durables" "Energy" "Energy"
## [142] "Public Utilities" "Consumer Non-Durables" "Energy"
## [145] "Basic Industries" "Consumer Non-Durables" "Finance"
## [148] "Finance" "Capital Goods" "Consumer Services"
## [151] "Energy" "Health Care" "Capital Goods"
## [154] "Capital Goods" "Consumer Services" "Capital Goods"
## [157] "Technology" "Transportation" "Finance"
## [160] "Consumer Non-Durables" "Consumer Services" "Finance"
## [163] "Consumer Services" "Public Utilities" "Technology"
## [166] "Basic Industries" "Public Utilities" "Public Utilities"
## [169] "Consumer Services" "Basic Industries" "Technology"
## [172] "Basic Industries" "Energy" "Public Utilities"
## [175] "Health Care" "Health Care" "Health Care"
## [178] "Energy" "Energy" "Public Utilities"
## [181] "Public Utilities" "Energy" "Public Utilities"
## [184] "Public Utilities" "Energy" "Technology"
## [187] "Finance" "Energy" "Consumer Services"
## [190] "Consumer Services" "Consumer Services" "Consumer Non-Durables"
## [193] "Finance" "Public Utilities" "Public Utilities"
## [196] "Public Utilities" "Consumer Services" "Energy"
## [199] "Technology" "Miscellaneous" "Consumer Services"
## [202] "Transportation" "Capital Goods" "Capital Goods"
## [205] "Finance" "Miscellaneous" "Public Utilities"
## [208] "Miscellaneous" "Basic Industries" "Consumer Non-Durables"
## [211] "Capital Goods" "Public Utilities" "Capital Goods"
## [214] "Basic Industries" "Finance" "Basic Industries"
## [217] "Health Care" "Consumer Services" "Capital Goods"
## [220] "Energy" "Consumer Non-Durables" "Capital Goods"
## [223] "Capital Goods" "Health Care" "Miscellaneous"
## [226] "Finance" "Technology" "Finance"
## [229] "Energy" "Finance" "Health Care"
## [232] "Consumer Services" "Finance" "Capital Goods"
## [235] "Consumer Non-Durables" "Energy" "Technology"
## [238] "Consumer Services" "Consumer Services" "Capital Goods"
## [241] "Capital Goods" "Consumer Non-Durables" "Consumer Services"
## [244] "Technology" "Finance" "Health Care"
## [247] "Finance" "Capital Goods" "Technology"
## [250] "Technology" "Technology" "Finance"
## [253] "Capital Goods" "Finance" "Consumer Services"
## [256] "Technology" "Basic Industries" "Basic Industries"
## [259] "Finance" "Health Care" "Finance"
## [262] "Finance" "Consumer Non-Durables" "Basic Industries"
## [265] "Health Care" "Consumer Services" "Transportation"
## [268] "Finance" "Consumer Non-Durables" "Consumer Non-Durables"
## [271] "Finance" "Capital Goods" "Consumer Durables"
## [274] "Public Utilities" "Finance" "Energy"
## [277] "Public Utilities" "Consumer Services" "Capital Goods"
## [280] "Health Care" "Consumer Non-Durables" "Consumer Services"
## [283] "Technology" "Basic Industries" "Finance"
## [286] "Basic Industries" "Consumer Services" "Finance"
## [289] "Capital Goods" "Finance" "Consumer Services"
## [292] "Basic Industries" "Finance" "Energy"
## [295] "Capital Goods" "Finance" "Energy"
## [298] "Energy" "Finance" "Finance"
## [301] "Basic Industries" "Basic Industries" "Miscellaneous"
## [304] "Consumer Non-Durables" "Consumer Services" "Health Care"
## [307] "Health Care" "Health Care" "Finance"
## [310] "Capital Goods" "Consumer Services" "Consumer Services"
## [313] "Finance" "Finance" "Consumer Non-Durables"
## [316] "Finance" "Finance" "Technology"
## [319] "Energy" "Miscellaneous" "Public Utilities"
## [322] "Consumer Services" "Basic Industries" "Public Utilities"
## [325] "Consumer Non-Durables" "Public Utilities" "Energy"
## [328] "Technology" "Finance" "Transportation"
## [331] "Capital Goods" "Consumer Services" "Health Care"
## [334] "Health Care" "Basic Industries" "Basic Industries"
## [337] "Capital Goods" "Energy" "Technology"
## [340] "Public Utilities" "Technology" "Public Utilities"
## [343] "Finance" "Technology" "Technology"
## [346] "Capital Goods" "Technology" "Energy"
## [349] "Energy" "Energy" "Health Care"
## [352] "Consumer Non-Durables" "Energy" "Energy"
## [355] "Public Utilities" "Technology" "Energy"
## [358] "Energy" "Finance" "Basic Industries"
## [361] "Basic Industries" "Public Utilities" "Basic Industries"
## [364] "Finance" "Consumer Services" "Finance"
## [367] "Finance" "Public Utilities" "Public Utilities"
## [370] "Consumer Services" "Health Care" "Finance"
## [373] "Capital Goods" "Consumer Services" "Finance"
## [376] "Consumer Services" "Public Utilities" "Health Care"
## [379] "Consumer Services" "Technology" "Basic Industries"
## [382] "Capital Goods" "Consumer Services" "Finance"
## [385] "Capital Goods" "Finance" "Finance"
## [388] "Consumer Services" "Finance" "Technology"
## [391] "Technology" "Energy" "Energy"
## [394] "Consumer Services" "Public Utilities" "Technology"
## [397] "Consumer Services" "Finance" "Technology"
## [400] "Consumer Services" "Public Utilities" "Consumer Services"
## [403] "Health Care" "Technology" "Consumer Non-Durables"
## [406] "Public Utilities" "Basic Industries" "Transportation"
## [409] "Consumer Services" "Public Utilities" "Technology"
## [412] "Capital Goods" "Finance" "Health Care"
## [415] "Technology" "Health Care" "Finance"
## [418] "Consumer Services" "Finance" "Energy"
## [421] "Finance" "Finance" "Consumer Non-Durables"
## [424] "Technology" "Health Care" "Consumer Services"
## [427] "Consumer Services" "Public Utilities" "Technology"
## [430] "Energy" "Capital Goods" "Health Care"
## [433] "Public Utilities" "Public Utilities" "Public Utilities"
## [436] "Basic Industries" "Consumer Services" "Capital Goods"
## [439] "Basic Industries" "Finance" "Finance"
## [442] "Health Care" "Finance" "Capital Goods"
## [445] "Consumer Services" "Consumer Services" "Consumer Services"
## [448] "Finance" "Energy" "Miscellaneous"
## [451] "Capital Goods" "Capital Goods" "Finance"
## [454] "Technology" "Technology" "Consumer Non-Durables"
## [457] "Finance" "Technology" "Finance"
## [460] "Consumer Services" "Public Utilities" "Basic Industries"
## [463] "Basic Industries" "Transportation" "Transportation"
## [466] "Capital Goods" "Health Care" "Health Care"
## [469] "Consumer Non-Durables" "Basic Industries" "Energy"
## [472] "Technology" "Consumer Services" "Public Utilities"
## [475] "Consumer Services" "Miscellaneous" "Public Utilities"
## [478] "Technology" "Consumer Services" "Basic Industries"
## [481] "Consumer Services" "Finance" "Consumer Services"
## [484] "Consumer Services" "Consumer Services" "Public Utilities"
## [487] "Public Utilities" "Capital Goods" "Consumer Services"
## [490] "Public Utilities" "Health Care" "Finance"
## [493] "Consumer Services" "Basic Industries" "Public Utilities"
## [496] "Capital Goods" "Finance" "Consumer Services"
## [499] "Basic Industries" "Public Utilities" "Technology"
## [502] "Technology" "Capital Goods" "Consumer Services"
## [505] "Consumer Services" "Health Care" "Health Care"
## [508] "Transportation"
Based on this dataset, create a table and a bar plot that shows the number of companies per sector, in descending order
# a easier way
# nyse %>%
# select(sector) %>%
# table() %>%
# sort(decreasing = T) %>%
# barplot()
nyse %>%
group_by(sector) %>%
mutate(company_num = count(sector)) %>%
ggplot(aes(x=company_num, y=fct_reorder(sector, company_num))) +
geom_bar(stat="identity") +
labs(title = "Number of Companies in each Sector",
x = "",
y = "Sector")Next, let’s choose some stocks and their ticker symbols and download some data. You MUST choose 6 different stocks from the ones listed below; You should, however, add SPY which is the SP500 ETF (Exchange Traded Fund).
# Notice the cache=TRUE argument inthe chunk options. Because getting data is time consuming,
# cache=TRUE means that once it downloads data, the chunk will not run again next time you knit your Rmd
myStocks <- c("EBR","BBL","AEE","BCE","BRO","CAT","BUD","SPY" ) %>%
tq_get(get = "stock.prices",
from = "2011-01-01",
to = "2021-08-31") %>%
group_by(symbol)
glimpse(myStocks) # examine the structure of the resulting data frame## Rows: 21,464
## Columns: 8
## Groups: symbol [8]
## $ symbol <chr> "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR"…
## $ date <date> 2011-01-03, 2011-01-04, 2011-01-05, 2011-01-06, 2011-01-07, …
## $ open <dbl> 13.9, 14.1, 14.3, 14.3, 14.0, 13.8, 13.8, 13.8, 13.9, 14.0, 1…
## $ high <dbl> 14.0, 14.3, 14.5, 14.3, 14.0, 13.9, 13.8, 13.9, 14.1, 14.3, 1…
## $ low <dbl> 13.9, 14.0, 14.3, 14.0, 13.8, 13.7, 13.6, 13.7, 13.8, 13.9, 1…
## $ close <dbl> 14.0, 14.2, 14.4, 14.1, 14.0, 13.7, 13.7, 13.8, 14.0, 14.2, 1…
## $ volume <dbl> 878300, 802900, 950300, 855800, 586000, 1020300, 1545800, 112…
## $ adjusted <dbl> 9.85, 10.00, 10.13, 9.92, 9.82, 9.63, 9.65, 9.71, 9.84, 10.03…
We can see that the dataset is 8x21,464 tibble with each row being the ohlc (open,high,low,close) and volume for a stock on a given date.
Financial performance analysis depend on returns; If I buy a stock today for 100 and I sell it tomorrow for 101.75, my one-day return, assuming no transaction costs, is 1.75%. So given the adjusted closing prices, our first step is to calculate daily and monthly returns.
#calculate daily returns
myStocks_returns_daily <- myStocks %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "daily",
type = "log",
col_rename = "daily_returns",
cols = c(nested.col))
#calculate monthly returns
myStocks_returns_monthly <- myStocks %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "arithmetic",
col_rename = "monthly_returns",
cols = c(nested.col))
#calculate yearly returns
myStocks_returns_annual <- myStocks %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "yearly",
type = "arithmetic",
col_rename = "yearly_returns",
cols = c(nested.col))Create a table where you summarise monthly returns for each of the stocks and SPY; min, max, median, mean, SD.
glimpse(myStocks_returns_monthly)## Rows: 1,024
## Columns: 3
## Groups: symbol [8]
## $ symbol <chr> "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR", "EBR"…
## $ date <date> 2011-01-31, 2011-02-28, 2011-03-31, 2011-04-29, 2011-…
## $ monthly_returns <dbl> -0.0264, 0.0487, 0.0861, -0.0445, -0.0263, -0.0264, -0…
monthlystocks_summarised <- myStocks_returns_monthly %>%
group_by(symbol) %>%
summarise(min_return = min(monthly_returns),
max_return = max(monthly_returns),
median_return = median(monthly_returns),
mean_return = mean(monthly_returns),
sd_return = sd(monthly_returns))Plot a density plot, using geom_density(), for each of the stocks
ggplot(myStocks_returns_monthly, aes(x = monthly_returns)) +
geom_density() +
facet_wrap(~symbol) +
labs(
title = "Density Plots for Monthly Returns of Stocks",
x = "Monthly Return",
y = "Density"
) +
NULLWhat can you infer from this plot? Which stock is the riskiest? The least risky?
TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.
The monthly returns for the flatter density plots are more dispersed whereas those with tall peaks are more concentrated. The riskiest stock is EBR and the least risky is SPY (as an ETF) due to the shape of their peaks.
Finally, make a plot that shows the expected monthly return (mean) of a stock on the Y axis and the risk (standard deviation) in the X-axis. Please use ggrepel::geom_text_repel() to label each stock
monthlystocks_summarised%>%
ggplot(aes(y = mean_return, x=sd_return)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = symbol)) +
NULLWhat can you infer from this plot? Are there any stocks which, while being riskier, do not have a higher expected return?
TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.
EBR is the most risky as it has the highest standard deviation of returns. EBR has the highest sd but also a high expected return. AEE, CAT and SPY also have high expected returns with less standard deviation. BBL and BUD have lower expected returns and higher standard deviations meaning they are riskier and do not have high expected returns. SPY produces a good return with lower risk.
For this task, you will analyse a data set on Human Resource Analytics. The IBM HR Analytics Employee Attrition & Performance data set is a fictional data set created by IBM data scientists. Among other things, the data set includes employees’ income, their distance from work, their position in the company, their level of education, etc. A full description can be found on the website.
First let us load the data
hr_dataset <- read_csv(here::here("data", "datasets_1067_1925_WA_Fn-UseC_-HR-Employee-Attrition.csv"))
glimpse(hr_dataset)## Rows: 1,470
## Columns: 35
## $ Age <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition <chr> "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ BusinessTravel <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269…
## $ MonthlyRate <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, …
## $ Over18 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …
I am going to clean the data set, as variable names are in capital letters, some variables are not really necessary, and some variables, e.g., education are given as a number rather than a more useful description
hr_cleaned <- hr_dataset %>%
clean_names() %>%
mutate(
education = case_when(
education == 1 ~ "Below College",
education == 2 ~ "College",
education == 3 ~ "Bachelor",
education == 4 ~ "Master",
education == 5 ~ "Doctor"
),
environment_satisfaction = case_when(
environment_satisfaction == 1 ~ "Low",
environment_satisfaction == 2 ~ "Medium",
environment_satisfaction == 3 ~ "High",
environment_satisfaction == 4 ~ "Very High"
),
job_satisfaction = case_when(
job_satisfaction == 1 ~ "Low",
job_satisfaction == 2 ~ "Medium",
job_satisfaction == 3 ~ "High",
job_satisfaction == 4 ~ "Very High"
),
performance_rating = case_when(
performance_rating == 1 ~ "Low",
performance_rating == 2 ~ "Good",
performance_rating == 3 ~ "Excellent",
performance_rating == 4 ~ "Outstanding"
),
work_life_balance = case_when(
work_life_balance == 1 ~ "Bad",
work_life_balance == 2 ~ "Good",
work_life_balance == 3 ~ "Better",
work_life_balance == 4 ~ "Best"
)
) %>%
select(age, attrition, daily_rate, department,
distance_from_home, education,
gender, job_role,environment_satisfaction,
job_satisfaction, marital_status,
monthly_income, num_companies_worked, percent_salary_hike,
performance_rating, total_working_years,
work_life_balance, years_at_company,
years_since_last_promotion)glimpse(hr_cleaned)## Rows: 1,470
## Columns: 19
## $ age <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35,…
## $ attrition <chr> "Yes", "No", "Yes", "No", "No", "No", "No",…
## $ daily_rate <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 135…
## $ department <chr> "Sales", "Research & Development", "Researc…
## $ distance_from_home <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26…
## $ education <chr> "College", "Below College", "College", "Mas…
## $ gender <chr> "Female", "Male", "Male", "Female", "Male",…
## $ job_role <chr> "Sales Executive", "Research Scientist", "L…
## $ environment_satisfaction <chr> "Medium", "High", "Very High", "Very High",…
## $ job_satisfaction <chr> "Very High", "Medium", "High", "High", "Med…
## $ marital_status <chr> "Single", "Married", "Single", "Married", "…
## $ monthly_income <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 2…
## $ num_companies_worked <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5…
## $ percent_salary_hike <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13,…
## $ performance_rating <chr> "Excellent", "Outstanding", "Excellent", "E…
## $ total_working_years <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5,…
## $ work_life_balance <chr> "Bad", "Better", "Better", "Better", "Bette…
## $ years_at_company <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, …
## $ years_since_last_promotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0…
Produce a one-page summary describing this dataset. Here is a non-exhaustive list of questions:
attrition)# 1233 employees stay while 237 left (19.2%).
hr_cleaned %>%
group_by(attrition)%>%
summarise(count = count(attrition))## # A tibble: 2 × 2
## attrition count
## <chr> <int>
## 1 No 1233
## 2 Yes 237
# to see the how attrition rate changes with years at company
prop.table(table(hr_cleaned[,c("years_at_company","attrition")]),1)[,2]%>%
barplot(main="Attrition Rate vs Years At Company")
abline(h=0.192,col="red") # avergae attrition rateage, years_at_company, monthly_income and years_since_last_promotion distributed? can you roughly guess which of these variables is closer to Normal just by looking at summary statistics?skim(hr_cleaned)| Name | hr_cleaned |
| Number of rows | 1470 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| attrition | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| department | 0 | 1 | 5 | 22 | 0 | 3 | 0 |
| education | 0 | 1 | 6 | 13 | 0 | 5 | 0 |
| gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| job_role | 0 | 1 | 7 | 25 | 0 | 9 | 0 |
| environment_satisfaction | 0 | 1 | 3 | 9 | 0 | 4 | 0 |
| job_satisfaction | 0 | 1 | 3 | 9 | 0 | 4 | 0 |
| marital_status | 0 | 1 | 6 | 8 | 0 | 3 | 0 |
| performance_rating | 0 | 1 | 9 | 11 | 0 | 2 | 0 |
| work_life_balance | 0 | 1 | 3 | 6 | 0 | 4 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | 0 | 1 | 36.92 | 9.14 | 18 | 30 | 36 | 43 | 60 | ▂▇▇▃▂ |
| daily_rate | 0 | 1 | 802.49 | 403.51 | 102 | 465 | 802 | 1157 | 1499 | ▇▇▇▇▇ |
| distance_from_home | 0 | 1 | 9.19 | 8.11 | 1 | 2 | 7 | 14 | 29 | ▇▅▂▂▂ |
| monthly_income | 0 | 1 | 6502.93 | 4707.96 | 1009 | 2911 | 4919 | 8379 | 19999 | ▇▅▂▁▂ |
| num_companies_worked | 0 | 1 | 2.69 | 2.50 | 0 | 1 | 2 | 4 | 9 | ▇▃▂▂▁ |
| percent_salary_hike | 0 | 1 | 15.21 | 3.66 | 11 | 12 | 14 | 18 | 25 | ▇▅▃▂▁ |
| total_working_years | 0 | 1 | 11.28 | 7.78 | 0 | 6 | 10 | 15 | 40 | ▇▇▂▁▁ |
| years_at_company | 0 | 1 | 7.01 | 6.13 | 0 | 3 | 5 | 9 | 40 | ▇▂▁▁▁ |
| years_since_last_promotion | 0 | 1 | 2.19 | 3.22 | 0 | 0 | 1 | 3 | 15 | ▇▁▁▁▁ |
job_satisfaction and work_life_balance distributed? Don’t just report counts, but express categories as % of total?hr_cleaned %>%
group_by(job_satisfaction)%>%
summarise(countjs = n(),
percentagejs = countjs/nrow(hr_cleaned)*100)## # A tibble: 4 × 3
## job_satisfaction countjs percentagejs
## <chr> <int> <dbl>
## 1 High 442 30.1
## 2 Low 289 19.7
## 3 Medium 280 19.0
## 4 Very High 459 31.2
hr_cleaned %>%
group_by(work_life_balance)%>%
summarise(countwlb= n(),
percentagewlb = round(countwlb/nrow(hr_cleaned)*100,2))## # A tibble: 4 × 3
## work_life_balance countwlb percentagewlb
## <chr> <int> <dbl>
## 1 Bad 80 5.44
## 2 Best 153 10.4
## 3 Better 893 60.8
## 4 Good 344 23.4
ggplot(hr_cleaned, aes(x = fct_relevel(education,
"Doctor", "Master", "Bachelor",
"College", "Below College"), y = monthly_income)) +
geom_boxplot()+
labs(title = "Boxplot of Monthly Income against Education",
x = "Education",
y = "Monthly Income")+
NULLggplot(hr_cleaned, aes(x = monthly_income, y = gender)) +
geom_boxplot()+
labs(title = "Boxplot of Monthly Income against Gender",
x = "Monthly Income",
y = "Gender")+
NULLggplot(hr_cleaned, aes(x=fct_reorder(job_role,-monthly_income), y=monthly_income)) +
geom_boxplot() +
labs(title = "Boxplot of Monthly Income against Job Role",
x = "Job Role",
y = "Monthly Income")+
NULLhr_cleaned %>%
group_by(education) %>%
summarise(medianinc = median(monthly_income),
meaninc = mean(monthly_income)) %>%
ggplot(aes(x = fct_relevel(education,
"Doctor", "Master", "Bachelor",
"College", "Below College"),
y=meaninc)) +
geom_bar(stat = "identity") +
labs(title = "Average Monthly Income of Each Education Level",
x = "Education",
y = "Average Monthly Income")+
NULLggthemeshr_cleaned %>%
ggplot(aes(x=monthly_income)) +
geom_histogram(bins=30)+
facet_wrap(~fct_relevel(education,
"Doctor", "Master", "Bachelor",
"College", "Below College")) +
theme_wsj() +
NULLjob_rolehr_cleaned %>%
ggplot(aes(y=monthly_income, x=age)) +
geom_point() +
geom_smooth(method="lm")+
facet_wrap(~job_role) +
theme_wsj() +
NULLThe purpose of this exercise is to reproduce a plot using your dplyr and ggplot2 skills. Read the article The Racial Factor: There’s 77 Counties Which Are Deep Blue But Also Low-Vaxx. Guess What They Have In Common? and have a look at the attached figure.
You dont have to worry about the blue-red backgound and don’t worry about replicating it exactly, try and see how far you can get. You’re encouraged to work together if you want to and exchange tips/tricks you figured out– and even though the figure in the original article is from early July 2021, you can use the most recent data.
Some hints to get you started:
# 3154 unique fips code in election2020_results
length(unique(election2020_results$fips))## [1] 3154
# check unique values of candidate names
unique(election2020_results$candidate)## [1] "JOSEPH R BIDEN JR" "OTHER" "DONALD J TRUMP"
## [4] "JO JORGENSEN"
data <- election2020_results %>%
mutate(votes_percentage = candidatevotes/totalvotes) %>% # calculate percentage
filter(candidate=="DONALD J TRUMP", mode=="TOTAL") %>% # we only need Trump votes
inner_join(vaccinations,by = "fips") %>% # inner join with vaccinations data
inner_join(population,by = "fips") # inner join with population data
# generate graph below
# install.packages("ggpubr") # to show equation easily
library(ggpubr)
# calculate actual vaccination percentage
actual = sum(data$series_complete_yes)/sum(data$pop_estimate_2019)
data %>%
# filter out 0% vaccinated points
filter(series_complete_pop_pct>0) %>%
ggplot() +
geom_point(aes(x=votes_percentage,y=series_complete_pop_pct/100,size=pop_estimate_2019),shape=21,fill="light blue")+
# scale circle size
scale_size(range = c(0, 20)) +
# add points in the center of circles
geom_point(aes(x=votes_percentage,y=series_complete_pop_pct/100),size=0.5)+
# add regression line
geom_smooth(aes(x=votes_percentage,y=series_complete_pop_pct/100),
method="lm",linetype="dotted",colour="blue",se=FALSE)+
# add the equation of regression line
stat_regline_equation(aes(x=votes_percentage,y=series_complete_pop_pct/100),
label.y = 0.1,colour="red",fontface = "bold.italic") +
# add r square
stat_cor(aes(x=votes_percentage,y=series_complete_pop_pct/100,label = paste(..rr.label..)),
label.y = 0.05,colour="red")+
# add date
geom_text(aes(x=0.3, y=0.07,label = "09/03/2021",
fontface = "bold.italic"),colour="red")+
# add horizonal lines below
geom_hline(aes(yintercept=0.85), linetype=2) + # herd immunity line
geom_text(aes(x=0, y=0.85, label = "Herd Immunity threshold (?)",
vjust=-1, hjust=0, fontface = "bold.italic"),colour="blue") +
geom_hline(aes(yintercept=0.539), linetype=2) + # Target line
geom_text(aes(x=0, y=0.539,label = "TARGET: 53.9%",
vjust=-1,hjust=0, fontface = "bold.italic"),colour="blue") +
geom_hline(aes(yintercept=actual), linetype=2) + # actual line
geom_text(aes(x=0, y=actual, label = paste("ACTUAL: ", round(actual*100,1),"%"),
vjust=-1,hjust=0,fontface = "bold.italic"),
colour="blue") +
# adjuct grid lines
scale_x_continuous(label=scales::percent_format(accuracy = 1),
breaks=seq(0,1,0.05),limits = c(0,1))+
scale_y_continuous(label=scales::percent_format(accuracy = 1),
breaks=seq(0,1,0.05),limits = c(0,1))+
geom_text(aes(x=0.5,y=1, label = "EACH U.S. COUNTY",vjust=0.5,hjust=0.5,
family="mono",fontface = "bold"),
size=5)+
labs(title = "COVID-19 VACCINATION LEVELS OUT OF TOTAL POPULATION BY COUNTY",
x = "2020 Trump Vote %",
y = "% of Total Population Vaccinated")+
# remove the legend
theme(legend.position = "none") +
NULLThe Guardian newspaper has an election poll tracker for the upcoming German election. The list of the opinion polls since Jan 2021 can be found at Wikipedia and your task is to reproduce the graph similar to the one produced by the Guardian.
The following code will scrape the wikipedia page and import the table in a dataframe.
url <- "https://en.wikipedia.org/wiki/Opinion_polling_for_the_2021_German_federal_election"
# https://www.economist.com/graphic-detail/who-will-succeed-angela-merkel
# https://www.theguardian.com/world/2021/jun/21/german-election-poll-tracker-who-will-be-the-next-chancellor
# get tables that exist on wikipedia page
tables <- url %>%
read_html() %>%
html_nodes(css="table")
# parse HTML tables into a dataframe called polls
# Use purr::map() to create a list of all tables in URL
polls <- map(tables, . %>%
html_table(fill=TRUE)%>%
janitor::clean_names())
# list of opinion polls
german_election_polls <- polls[[1]] %>% # the first table on the page contains the list of all opinions polls
slice(2:(n()-1)) %>% # drop the first row, as it contains again the variable names and last row that contains 2017 results
mutate(
# polls are shown to run from-to, e.g. 9-13 Aug 2021. We keep the last date, 13 Aug here, as the poll date
# and we extract it by picking the last 11 characters from that field
end_date = str_sub(fieldwork_date, -11),
# end_date is still a string, so we convert it into a date object using lubridate::dmy()
end_date = dmy(end_date),
# we also get the month and week number from the date, if we want to do analysis by month- week, etc.
month = month(end_date),
week = isoweek(end_date)
)
stack(german_election_polls)## values ind
## 1 Forschungsgruppe Wahlen polling_firm
## 2 Infratest dimap polling_firm
## 3 Wahlkreisprognose polling_firm
## 4 YouGov polling_firm
## 5 Kantar polling_firm
## 6 INSA polling_firm
## 7 Forsa polling_firm
## 8 Ipsos polling_firm
## 9 INSA polling_firm
## 10 Forschungsgruppe Wahlen polling_firm
## 11 Allensbach polling_firm
## 12 Civey polling_firm
## 13 YouGov polling_firm
## 14 Kantar polling_firm
## 15 INSA polling_firm
## 16 Forsa polling_firm
## 17 INSA polling_firm
## 18 Infratest dimap polling_firm
## 19 Trend Research polling_firm
## 20 Civey polling_firm
## 21 Wahlkreisprognose polling_firm
## 22 Kantar polling_firm
## 23 Allensbach polling_firm
## 24 INSA polling_firm
## 25 Forsa polling_firm
## 26 INSA polling_firm
## 27 Forschungsgruppe Wahlen polling_firm
## 28 Civey polling_firm
## 29 Kantar polling_firm
## 30 INSA polling_firm
## 31 Forsa polling_firm
## 32 INSA polling_firm
## 33 Infratest dimap polling_firm
## 34 Civey polling_firm
## 35 Kantar polling_firm
## 36 INSA polling_firm
## 37 Forsa polling_firm
## 38 Ipsos polling_firm
## 39 INSA polling_firm
## 40 Forschungsgruppe Wahlen polling_firm
## 41 Civey polling_firm
## 42 GMS polling_firm
## 43 Kantar polling_firm
## 44 YouGov polling_firm
## 45 INSA polling_firm
## 46 Forsa polling_firm
## 47 INSA polling_firm
## 48 Allensbach polling_firm
## 49 Infratest dimap polling_firm
## 50 Civey polling_firm
## 51 Kantar polling_firm
## 52 INSA polling_firm
## 53 Forsa polling_firm
## 54 INSA polling_firm
## 55 Forschungsgruppe Wahlen polling_firm
## 56 Civey polling_firm
## 57 Allensbach (intermediate result) polling_firm
## 58 Kantar polling_firm
## 59 INSA polling_firm
## 60 Forsa polling_firm
## 61 INSA polling_firm
## 62 Civey polling_firm
## 63 Kantar polling_firm
## 64 INSA polling_firm
## 65 Forsa polling_firm
## 66 INSA polling_firm
## 67 Infratest dimap polling_firm
## 68 Civey polling_firm
## 69 Kantar polling_firm
## 70 YouGov polling_firm
## 71 INSA polling_firm
## 72 Forsa polling_firm
## 73 Ipsos polling_firm
## 74 INSA polling_firm
## 75 Forschungsgruppe Wahlen polling_firm
## 76 Infratest dimap polling_firm
## 77 Civey polling_firm
## 78 Kantar polling_firm
## 79 Forsa polling_firm
## 80 INSA polling_firm
## 81 GMS polling_firm
## 82 Forsa polling_firm
## 83 INSA polling_firm
## 84 Civey polling_firm
## 85 Kantar polling_firm
## 86 INSA polling_firm
## 87 Forsa polling_firm
## 88 INSA polling_firm
## 89 Redfield & Wilton Strategies polling_firm
## 90 Infratest dimap polling_firm
## 91 Forschungsgruppe Wahlen polling_firm
## 92 Civey polling_firm
## 93 Allensbach polling_firm
## 94 Kantar polling_firm
## 95 INSA polling_firm
## 96 Forsa polling_firm
## 97 INSA polling_firm
## 98 Civey polling_firm
## 99 Kantar polling_firm
## 100 INSA polling_firm
## 101 Forsa polling_firm
## 102 INSA polling_firm
## 103 INSA polling_firm
## 104 Kantar polling_firm
## 105 Civey polling_firm
## 106 Ipsos polling_firm
## 107 YouGov polling_firm
## 108 Forsa polling_firm
## 109 INSA polling_firm
## 110 Forschungsgruppe Wahlen polling_firm
## 111 Civey polling_firm
## 112 Kantar polling_firm
## 113 INSA polling_firm
## 114 Forsa polling_firm
## 115 GMS polling_firm
## 116 INSA polling_firm
## 117 Allensbach polling_firm
## 118 Infratest dimap polling_firm
## 119 Kantar polling_firm
## 120 INSA polling_firm
## 121 Forsa polling_firm
## 122 Redfield & Wilton Strategies polling_firm
## 123 Forschungsgruppe Wahlen polling_firm
## 124 Infratest dimap polling_firm
## 125 Kantar polling_firm
## 126 Civey polling_firm
## 127 INSA polling_firm
## 128 Forsa polling_firm
## 129 Kantar polling_firm
## 130 INSA polling_firm
## 131 YouGov polling_firm
## 132 Forsa polling_firm
## 133 Civey polling_firm
## 134 INSA (intermediate result) polling_firm
## 135 Kantar polling_firm
## 136 Civey polling_firm
## 137 Forsa (intermediate result) polling_firm
## 138 INSA polling_firm
## 139 INSA polling_firm
## 140 Forsa polling_firm
## 141 INSA polling_firm
## 142 Forschungsgruppe Wahlen polling_firm
## 143 Allensbach polling_firm
## 144 Infratest dimap polling_firm
## 145 Kantar polling_firm
## 146 INSA polling_firm
## 147 Forsa polling_firm
## 148 Kantar polling_firm
## 149 INSA polling_firm
## 150 Forsa polling_firm
## 151 Kantar polling_firm
## 152 Infratest dimap polling_firm
## 153 INSA polling_firm
## 154 YouGov polling_firm
## 155 GMS polling_firm
## 156 Forsa polling_firm
## 157 Forschungsgruppe Wahlen polling_firm
## 158 Kantar polling_firm
## 159 Civey polling_firm
## 160 INSA polling_firm
## 161 Forsa polling_firm
## 162 Allensbach polling_firm
## 163 INSA (intermediate result) polling_firm
## 164 Infratest dimap polling_firm
## 165 Kantar polling_firm
## 166 Civey polling_firm
## 167 INSA polling_firm
## 168 Forsa polling_firm
## 169 Kantar polling_firm
## 170 INSA polling_firm
## 171 Forsa polling_firm
## 172 Kantar polling_firm
## 173 Infratest dimap polling_firm
## 174 INSA polling_firm
## 175 Forsa polling_firm
## 176 Forschungsgruppe Wahlen polling_firm
## 177 Kantar polling_firm
## 178 INSA polling_firm
## 179 YouGov polling_firm
## 180 Forsa polling_firm
## 181 Infratest dimap polling_firm
## 182 Kantar polling_firm
## 183 Allensbach polling_firm
## 184 INSA polling_firm
## 185 GMS polling_firm
## 186 Forsa polling_firm
## 187 Kantar polling_firm
## 188 INSA polling_firm
## 189 Forsa polling_firm
## 190 Infratest dimap polling_firm
## 191 Kantar polling_firm
## 192 YouGov polling_firm
## 193 INSA polling_firm
## 194 Forsa polling_firm
## 195 Forschungsgruppe Wahlen polling_firm
## 196 Kantar polling_firm
## 197 INSA polling_firm
## 198 Forsa polling_firm
## 199 Infratest dimap polling_firm
## 200 Kantar polling_firm
## 201 Allensbach polling_firm
## 202 INSA (intermediate result) polling_firm
## 203 INSA polling_firm
## 204 Forsa polling_firm
## 205 Forsa polling_firm
## 206 Forschungsgruppe Wahlen polling_firm
## 207 Kantar polling_firm
## 208 INSA polling_firm
## 209 Forsa polling_firm
## 210 Infratest dimap polling_firm
## 211 YouGov polling_firm
## 212 INSA polling_firm
## 213 GMS polling_firm
## 214 31 Aug–2 Sep 2021 fieldwork_date
## 215 30 Aug–1 Sep 2021 fieldwork_date
## 216 30–31 Aug 2021 fieldwork_date
## 217 27–31 Aug 2021 fieldwork_date
## 218 25–31 Aug 2021 fieldwork_date
## 219 27–30 Aug 2021 fieldwork_date
## 220 24–30 Aug 2021 fieldwork_date
## 221 28–29 Aug 2021 fieldwork_date
## 222 23–27 Aug 2021 fieldwork_date
## 223 24–26 Aug 2021 fieldwork_date
## 224 18–26 Aug 2021 fieldwork_date
## 225 18–25 Aug 2021 fieldwork_date
## 226 20–24 Aug 2021 fieldwork_date
## 227 18–24 Aug 2021 fieldwork_date
## 228 20–23 Aug 2021 fieldwork_date
## 229 16–23 Aug 2021 fieldwork_date
## 230 16–20 Aug 2021 fieldwork_date
## 231 17–18 Aug 2021 fieldwork_date
## 232 12–18 Aug 2021 fieldwork_date
## 233 11–18 Aug 2021 fieldwork_date
## 234 14–17 Aug 2021 fieldwork_date
## 235 11–17 Aug 2021 fieldwork_date
## 236 5–17 Aug 2021 fieldwork_date
## 237 13–16 Aug 2021 fieldwork_date
## 238 10–16 Aug 2021 fieldwork_date
## 239 9–13 Aug 2021 fieldwork_date
## 240 10–12 Aug 2021 fieldwork_date
## 241 4–11 Aug 2021 fieldwork_date
## 242 4–10 Aug 2021 fieldwork_date
## 243 6–9 Aug 2021 fieldwork_date
## 244 3–9 Aug 2021 fieldwork_date
## 245 2–6 Aug 2021 fieldwork_date
## 246 2–4 Aug 2021 fieldwork_date
## 247 28 Jul–4 Aug 2021 fieldwork_date
## 248 28 Jul–3 Aug 2021 fieldwork_date
## 249 30 Jul–2 Aug 2021 fieldwork_date
## 250 27 Jul–2 Aug 2021 fieldwork_date
## 251 21–31 Jul 2021 fieldwork_date
## 252 26–30 Jul 2021 fieldwork_date
## 253 27–29 Jul 2021 fieldwork_date
## 254 21–28 Jul 2021 fieldwork_date
## 255 21–27 Jul 2021 fieldwork_date
## 256 20–27 Jul 2021 fieldwork_date
## 257 23–26 Jul 2021 fieldwork_date
## 258 23–26 Jul 2021 fieldwork_date
## 259 20–26 Jul 2021 fieldwork_date
## 260 19–23 Jul 2021 fieldwork_date
## 261 3–22 Jul 2021 fieldwork_date
## 262 20–21 Jul 2021 fieldwork_date
## 263 14–21 Jul 2021 fieldwork_date
## 264 14–20 Jul 2021 fieldwork_date
## 265 16–19 Jul 2021 fieldwork_date
## 266 13–19 Jul 2021 fieldwork_date
## 267 12–16 Jul 2021 fieldwork_date
## 268 13–15 Jul 2021 fieldwork_date
## 269 7–14 Jul 2021 fieldwork_date
## 270 3–14 Jul 2021 fieldwork_date
## 271 7–13 Jul 2021 fieldwork_date
## 272 9–12 Jul 2021 fieldwork_date
## 273 6–12 Jul 2021 fieldwork_date
## 274 5–9 Jul 2021 fieldwork_date
## 275 30 Jun–7 Jul 2021 fieldwork_date
## 276 29 Jun–6 Jul 2021 fieldwork_date
## 277 2–5 Jul 2021 fieldwork_date
## 278 29 Jun–5 Jul 2021 fieldwork_date
## 279 28 Jun–2 Jul 2021 fieldwork_date
## 280 28–30 Jun 2021 fieldwork_date
## 281 23–30 Jun 2021 fieldwork_date
## 282 23–29 Jun 2021 fieldwork_date
## 283 25–28 Jun 2021 fieldwork_date
## 284 25–28 Jun 2021 fieldwork_date
## 285 22–28 Jun 2021 fieldwork_date
## 286 21–27 Jun 2021 fieldwork_date
## 287 21–25 Jun 2021 fieldwork_date
## 288 22–24 Jun 2021 fieldwork_date
## 289 22–23 Jun 2021 fieldwork_date
## 290 16–23 Jun 2021 fieldwork_date
## 291 15–22 Jun 2021 fieldwork_date
## 292 15–21 Jun 2021 fieldwork_date
## 293 18–21 Jun 2021 fieldwork_date
## 294 16–21 Jun 2021 fieldwork_date
## 295 15–21 Jun 2021 fieldwork_date
## 296 14–18 Jun 2021 fieldwork_date
## 297 9–16 Jun 2021 fieldwork_date
## 298 9–15 Jun 2021 fieldwork_date
## 299 11–14 Jun 2021 fieldwork_date
## 300 8–14 Jun 2021 fieldwork_date
## 301 7–11 Jun 2021 fieldwork_date
## 302 8–9 Jun 2021 fieldwork_date
## 303 7–9 Jun 2021 fieldwork_date
## 304 7–9 Jun 2021 fieldwork_date
## 305 2–9 Jun 2021 fieldwork_date
## 306 28 May–9 Jun 2021 fieldwork_date
## 307 2–8 Jun 2021 fieldwork_date
## 308 4–7 Jun 2021 fieldwork_date
## 309 1–7 Jun 2021 fieldwork_date
## 310 31 May–4 Jun 2021 fieldwork_date
## 311 26 May–2 Jun 2021 fieldwork_date
## 312 26 May–1 Jun 2021 fieldwork_date
## 313 28–31 May 2021 fieldwork_date
## 314 25–31 May 2021 fieldwork_date
## 315 25–28 May 2021 fieldwork_date
## 316 21–25 May 2021 fieldwork_date
## 317 19–25 May 2021 fieldwork_date
## 318 19–25 May 2021 fieldwork_date
## 319 18–25 May 2021 fieldwork_date
## 320 21–24 May 2021 fieldwork_date
## 321 18–21 May 2021 fieldwork_date
## 322 17–21 May 2021 fieldwork_date
## 323 18–20 May 2021 fieldwork_date
## 324 12–19 May 2021 fieldwork_date
## 325 11–18 May 2021 fieldwork_date
## 326 14–17 May 2021 fieldwork_date
## 327 11–17 May 2021 fieldwork_date
## 328 11–17 May 2021 fieldwork_date
## 329 10–14 May 2021 fieldwork_date
## 330 1–13 May 2021 fieldwork_date
## 331 10–11 May 2021 fieldwork_date
## 332 6–11 May 2021 fieldwork_date
## 333 7–10 May 2021 fieldwork_date
## 334 4–10 May 2021 fieldwork_date
## 335 5–6 May 2021 fieldwork_date
## 336 4–6 May 2021 fieldwork_date
## 337 3–5 May 2021 fieldwork_date
## 338 29 Apr–5 May 2021 fieldwork_date
## 339 28 Apr–5 May 2021 fieldwork_date
## 340 30 Apr–3 May 2021 fieldwork_date
## 341 27 Apr–3 May 2021 fieldwork_date
## 342 22–28 Apr 2021 fieldwork_date
## 343 23–26 Apr 2021 fieldwork_date
## 344 23–26 Apr 2021 fieldwork_date
## 345 20–26 Apr 2021 fieldwork_date
## 346 19–26 Apr 2021 fieldwork_date
## 347 23 Apr 2021 fieldwork_date
## 348 15–21 Apr 2021 fieldwork_date
## 349 14–21 Apr 2021 fieldwork_date
## 350 20 Apr 2021 fieldwork_date
## 351 20 Apr 2021 fieldwork_date
## 352 16–19 Apr 2021 fieldwork_date
## 353 13–16 Apr 2021 fieldwork_date
## 354 15 Apr 2021 fieldwork_date
## 355 13–15 Apr 2021 fieldwork_date
## 356 6–15 Apr 2021 fieldwork_date
## 357 13–14 Apr 2021 fieldwork_date
## 358 8–14 Apr 2021 fieldwork_date
## 359 9–12 Apr 2021 fieldwork_date
## 360 6–12 Apr 2021 fieldwork_date
## 361 31 Mar–7 Apr 2021 fieldwork_date
## 362 30 Mar–1 Apr 2021 fieldwork_date
## 363 30 Mar–1 Apr 2021 fieldwork_date
## 364 25–31 Mar 2021 fieldwork_date
## 365 29–30 Mar 2021 fieldwork_date
## 366 26–29 Mar 2021 fieldwork_date
## 367 25–29 Mar 2021 fieldwork_date
## 368 24–29 Mar 2021 fieldwork_date
## 369 23–29 Mar 2021 fieldwork_date
## 370 23–25 Mar 2021 fieldwork_date
## 371 18–24 Mar 2021 fieldwork_date
## 372 17–24 Mar 2021 fieldwork_date
## 373 19–22 Mar 2021 fieldwork_date
## 374 16–22 Mar 2021 fieldwork_date
## 375 8–21 Mar 2021 fieldwork_date
## 376 19 Mar 2021 fieldwork_date
## 377 15–17 Mar 2021 fieldwork_date
## 378 11–17 Mar 2021 fieldwork_date
## 379 10–17 Mar 2021 fieldwork_date
## 380 12–15 Mar 2021 fieldwork_date
## 381 9–15 Mar 2021 fieldwork_date
## 382 4–10 Mar 2021 fieldwork_date
## 383 5–8 Mar 2021 fieldwork_date
## 384 2–8 Mar 2021 fieldwork_date
## 385 25 Feb–3 Mar 2021 fieldwork_date
## 386 1–2 Mar 2021 fieldwork_date
## 387 26 Feb–1 Mar 2021 fieldwork_date
## 388 25 Feb–1 Mar 2021 fieldwork_date
## 389 23–25 Feb 2021 fieldwork_date
## 390 18–24 Feb 2021 fieldwork_date
## 391 19–22 Feb 2021 fieldwork_date
## 392 18–22 Feb 2021 fieldwork_date
## 393 16–22 Feb 2021 fieldwork_date
## 394 15–17 Feb 2021 fieldwork_date
## 395 11–17 Feb 2021 fieldwork_date
## 396 4–17 Feb 2021 fieldwork_date
## 397 12–15 Feb 2021 fieldwork_date
## 398 10–15 Feb 2021 fieldwork_date
## 399 9–15 Feb 2021 fieldwork_date
## 400 4–10 Feb 2021 fieldwork_date
## 401 5–8 Feb 2021 fieldwork_date
## 402 2–8 Feb 2021 fieldwork_date
## 403 1–3 Feb 2021 fieldwork_date
## 404 28 Jan–3 Feb 2021 fieldwork_date
## 405 29 Jan–1 Feb 2021 fieldwork_date
## 406 29 Jan–1 Feb 2021 fieldwork_date
## 407 26 Jan–1 Feb 2021 fieldwork_date
## 408 25–27 Jan 2021 fieldwork_date
## 409 21–27 Jan 2021 fieldwork_date
## 410 22–25 Jan 2021 fieldwork_date
## 411 18–25 Jan 2021 fieldwork_date
## 412 18–20 Jan 2021 fieldwork_date
## 413 14–20 Jan 2021 fieldwork_date
## 414 10–20 Jan 2021 fieldwork_date
## 415 17–18 Jan 2021 fieldwork_date
## 416 15–18 Jan 2021 fieldwork_date
## 417 16–17 Jan 2021 fieldwork_date
## 418 11–15 Jan 2021 fieldwork_date
## 419 12–14 Jan 2021 fieldwork_date
## 420 5–13 Jan 2021 fieldwork_date
## 421 8–11 Jan 2021 fieldwork_date
## 422 4–8 Jan 2021 fieldwork_date
## 423 4–6 Jan 2021 fieldwork_date
## 424 30 Dec 2020–5 Jan 2021 fieldwork_date
## 425 1–4 Jan 2021 fieldwork_date
## 426 29 Dec 2020–4 Jan 2021 fieldwork_date
## 427 1,301 samplesize
## 428 1,337 samplesize
## 429 2,340 samplesize
## 430 1,729 samplesize
## 431 1,439 samplesize
## 432 2,015 samplesize
## 433 2,508 samplesize
## 434 2,001 samplesize
## 435 1,247 samplesize
## 436 1,300 samplesize
## 437 1,038 samplesize
## 438 10,054 samplesize
## 439 1,689 samplesize
## 440 1,919 samplesize
## 441 2,119 samplesize
## 442 2,504 samplesize
## 443 1,352 samplesize
## 444 1,219 samplesize
## 445 1,798 samplesize
## 446 10,117 samplesize
## 447 2,005 samplesize
## 448 1,920 samplesize
## 449 1,018 samplesize
## 450 2,080 samplesize
## 451 2,501 samplesize
## 452 1,450 samplesize
## 453 1,252 samplesize
## 454 10,116 samplesize
## 455 1,446 samplesize
## 456 2,118 samplesize
## 457 2,509 samplesize
## 458 2,002 samplesize
## 459 1,312 samplesize
## 460 10,217 samplesize
## 461 1,413 samplesize
## 462 2,080 samplesize
## 463 2,502 samplesize
## 464 2,002 samplesize
## 465 1,196 samplesize
## 466 1,268 samplesize
## 467 10,111 samplesize
## 468 1,003 samplesize
## 469 1,441 samplesize
## 470 1,748 samplesize
## 471 2,007 samplesize
## 472 2,501 samplesize
## 473 1,316 samplesize
## 474 1,243 samplesize
## 475 1,188 samplesize
## 476 9,999 samplesize
## 477 1,421 samplesize
## 478 2,064 samplesize
## 479 2,503 samplesize
## 480 1,354 samplesize
## 481 1,224 samplesize
## 482 10,110 samplesize
## 483 1,028 samplesize
## 484 1,495 samplesize
## 485 2,087 samplesize
## 486 2,502 samplesize
## 487 1,352 samplesize
## 488 10,069 samplesize
## 489 1,405 samplesize
## 490 2,056 samplesize
## 491 2,505 samplesize
## 492 1,352 samplesize
## 493 1,317 samplesize
## 494 10,068 samplesize
## 495 1,418 samplesize
## 496 1,648 samplesize
## 497 2,060 samplesize
## 498 2,501 samplesize
## 499 2,002 samplesize
## 500 1,203 samplesize
## 501 1,271 samplesize
## 502 1,217 samplesize
## 503 10,087 samplesize
## 504 1,430 samplesize
## 505 2,502 samplesize
## 506 2,082 samplesize
## 507 1,002 samplesize
## 508 2,502 samplesize
## 509 1,502 samplesize
## 510 10,094 samplesize
## 511 1,413 samplesize
## 512 2,038 samplesize
## 513 2,501 samplesize
## 514 1,401 samplesize
## 515 1,500 samplesize
## 516 1,316 samplesize
## 517 1,232 samplesize
## 518 10,096 samplesize
## 519 1,070 samplesize
## 520 1,421 samplesize
## 521 2,015 samplesize
## 522 2,501 samplesize
## 523 1,380 samplesize
## 524 10,015 samplesize
## 525 1,419 samplesize
## 526 2,040 samplesize
## 527 2,500 samplesize
## 528 1,301 samplesize
## 529 2,128 samplesize
## 530 1,422 samplesize
## 531 10,016 samplesize
## 532 2,014 samplesize
## 533 1,706 samplesize
## 534 2,004 samplesize
## 535 1,405 samplesize
## 536 1,229 samplesize
## 537 10,050 samplesize
## 538 1,445 samplesize
## 539 2,023 samplesize
## 540 2,001 samplesize
## 541 1,005 samplesize
## 542 1,350 samplesize
## 543 1,027 samplesize
## 544 1,198 samplesize
## 545 1,428 samplesize
## 546 2,055 samplesize
## 547 2,501 samplesize
## 548 1,950 samplesize
## 549 1,271 samplesize
## 550 1,351 samplesize
## 551 1,910 samplesize
## 552 10,028 samplesize
## 553 2,075 samplesize
## 554 2,508 samplesize
## 555 1,442 samplesize
## 556 2,082 samplesize
## 557 1,643 samplesize
## 558 2,507 samplesize
## 559 10,039 samplesize
## 560 1,000 samplesize
## 561 1,225 samplesize
## 562 10,064 samplesize
## 563 1,502 samplesize
## 564 1,000 samplesize
## 565 3,057 samplesize
## 566 2,003 samplesize
## 567 1,007 samplesize
## 568 1,292 samplesize
## 569 1,051 samplesize
## 570 1,174 samplesize
## 571 1,437 samplesize
## 572 3,174 samplesize
## 573 2,500 samplesize
## 574 1,448 samplesize
## 575 3,020 samplesize
## 576 1,501 samplesize
## 577 1,429 samplesize
## 578 1,348 samplesize
## 579 3,049 samplesize
## 580 1,637 samplesize
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## 1608 10 fdp
## 1609 12 fdp
## 1610 11 fdp
## 1611 12 fdp
## 1612 11 fdp
## 1613 12 fdp
## 1614 10 fdp
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## 1617 11 fdp
## 1618 12 fdp
## 1619 12 fdp
## 1620 11 fdp
## 1621 12 fdp
## 1622 11 fdp
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## 1624 11 fdp
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## 1644 10.5 fdp
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## 1912 8 linke
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## 1936 17 grune
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## 1942 19 grune
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## 1944 19 grune
## 1945 20 grune
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## 1948 20 grune
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## 1956 18 grune
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## 1963 21 grune
## 1964 18 grune
## 1965 19.5 grune
## 1966 19 grune
## 1967 21 grune
## 1968 19 grune
## 1969 18 grune
## 1970 19 grune
## 1971 18 grune
## 1972 20 grune
## 1973 20 grune
## 1974 18 grune
## 1975 20 grune
## 1976 17 grune
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## 1981 18 grune
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## 1983 18 grune
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## 1985 22 grune
## 1986 20 grune
## 1987 19 grune
## 1988 18 grune
## 1989 20 grune
## 1990 21 grune
## 1991 19 grune
## 1992 22 grune
## 1993 21 grune
## 1994 22 grune
## 1995 19 grune
## 1996 21 grune
## 1997 19 grune
## 1998 20 grune
## 1999 21 grune
## 2000 20 grune
## 2001 21 grune
## 2002 20 grune
## 2003 19.5 grune
## 2004 21 grune
## 2005 20 grune
## 2006 15 grune
## 2007 20 grune
## 2008 22 grune
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## 2010 21.5 grune
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## 2012 20.5 grune
## 2013 22 grune
## 2014 21 grune
## 2015 22 grune
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## 2020 22 grune
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## 2557 3 lead
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## 2563 2 lead
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## 2566 Tie lead
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## 2568 Tie lead
## 2569 2 lead
## 2570 Tie lead
## 2571 Tie lead
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## 2573 Tie lead
## 2574 2 lead
## 2575 2 lead
## 2576 4 lead
## 2577 Tie lead
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## 2580 5 lead
## 2581 2 lead
## 2582 5 lead
## 2583 7 lead
## 2584 4 lead
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## 2589 8 lead
## 2590 4 lead
## 2591 2 lead
## 2592 9.5 lead
## 2593 6 lead
## 2594 7 lead
## 2595 9 lead
## 2596 7 lead
## 2597 5 lead
## 2598 12 lead
## 2599 8 lead
## 2600 12 lead
## 2601 9.5 lead
## 2602 5 lead
## 2603 9 lead
## 2604 10.5 lead
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## 2606 5 lead
## 2607 9 lead
## 2608 11 lead
## 2609 9 lead
## 2610 10 lead
## 2611 10 lead
## 2612 8 lead
## 2613 13.5 lead
## 2614 8 lead
## 2615 11 lead
## 2616 11 lead
## 2617 11 lead
## 2618 9 lead
## 2619 10 lead
## 2620 11 lead
## 2621 11 lead
## 2622 10 lead
## 2623 8 lead
## 2624 7 lead
## 2625 8 lead
## 2626 11 lead
## 2627 11.5 lead
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## 2629 7 lead
## 2630 9 lead
## 2631 7 lead
## 2632 7 lead
## 2633 8 lead
## 2634 9 lead
## 2635 8 lead
## 2636 9.5 lead
## 2637 8 lead
## 2638 8 lead
## 2639 8 lead
## 2640 9 lead
## 2641 7 lead
## 2642 8 lead
## 2643 7 lead
## 2644 7 lead
## 2645 5 lead
## 2646 8 lead
## 2647 6 lead
## 2648 8 lead
## 2649 8 lead
## 2650 5 lead
## 2651 6 lead
## 2652 5 lead
## 2653 5 lead
## 2654 7 lead
## 2655 2 lead
## 2656 4 lead
## 2657 1 lead
## 2658 3 lead
## 2659 4 lead
## 2660 2 lead
## 2661 4 lead
## 2662 2 lead
## 2663 4 lead
## 2664 1 lead
## 2665 1 lead
## 2666 1 lead
## 2667 4 lead
## 2668 2 lead
## 2669 2.5 lead
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german_election_polls %>%
ggplot() +
# union
geom_point(aes(x=end_date,y=union, colour="UNION"),shape=21)+
# span=0.2 to make the line less smoothed
geom_smooth(aes(x=end_date,y=union, colour="UNION"),se=F,span = 0.2)+
# spd
geom_point(aes(x=end_date,y=spd, colour="SPD"),shape=21)+
geom_smooth(aes(x=end_date,y=spd,colour="SPD"),se=F,span = 0.2)+
# afd
geom_point(aes(x=end_date,y=af_d,colour="AFD"),shape=21)+
geom_smooth(aes(x=end_date,y=af_d,colour="AFD"),se=F,span = 0.2)+
#fdp
geom_point(aes(x=end_date,y=fdp,colour="FDP"),shape=21)+
geom_smooth(aes(x=end_date,y=fdp,colour="FDP"),se=F,span = 0.2)+
#grune
geom_point(aes(x=end_date,y=grune, colour="GRUNE"),shape=21)+
geom_smooth(aes(x=end_date,y=grune,colour="GRUNE"),se=F,span = 0.2)+
#linke
geom_point(aes(x=end_date,y=linke,colour="LINKE"),shape=21)+
geom_smooth(aes(x=end_date,y=linke,colour="LINKE"),se=F,span = 0.2)+
#display every month
scale_x_date(date_labels="%b %y",date_breaks ="1 month")+
labs(
x="Date",
y="Votes %"
)+
scale_colour_manual("",
breaks = c("UNION","SPD","AFD","FDP","GRUNE","LINKE"),
values = c("black","red","blue","yellow","dark green","purple"))+
NULL?scale_colour_manualThere is a lot of explanatory text, comments, etc. You do not need these, so delete them and produce a stand-alone document that you could share with someone. Knit the edited and completed R Markdown file as an HTML document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.
Please seek out help when you need it, and remember the 15-minute rule. You know enough R (and have enough examples of code from class and your readings) to be able to do this. If you get stuck, ask for help from others, post a question on Slack– and remember that I am here to help too!
As a true test to yourself, do you understand the code you submitted and are you able to explain it to someone else?
Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analyzed.
Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow (both the code and the output).
Check plus (5/5): Finished all components of the assignment correctly and addressed both challenges. Code is well-documented (both self-documented and with additional comments as necessary). Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.